class_docstr: Implements RAdam optimization algorithm.
  
  Note:
      Deprecated, please use version provided by PyTorch_.
  
  It has been proposed in `On the Variance of the Adaptive Learning
  Rate and Beyond`__.
  
  Arguments:
      params: iterable of parameters to optimize or dicts defining
          parameter groups
      lr: learning rate (default: 1e-3)
      betas: coefficients used for computing
          running averages of gradient and its square (default: (0.9, 0.999))
      eps: term added to the denominator to improve
          numerical stability (default: 1e-8)
      weight_decay: weight decay (L2 penalty) (default: 0)
  
  Example:
      >>> import torch_optimizer as optim
      >>> optimizer = optim.RAdam(model.parameters(), lr=0.1)
      >>> optimizer.zero_grad()
      >>> loss_fn(model(input), target).backward()
      >>> optimizer.step()
  
  __ https://arxiv.org/abs/1908.03265
  
  Note:
      Reference code: https://github.com/LiyuanLucasLiu/RAdam
code: def __setstate__(self, state):
      super(RAdam, self).__setstate__(state)
  
file_path: pytorch-optimizer/torch_optimizer/radam.py
imports: import math
  import warnings
  import torch
  from torch.optim.optimizer import Optimizer
  from .types import Betas2, OptFloat, OptLossClosure, Params
label_desc: Use image node_id 2 for calling the RAdam obj's underlying member method code with example usage: obj.__setstate__(state) without return types